The Experts below are selected from a list of 189411 Experts worldwide ranked by ideXlab platform
Martin J Wainwright - One of the best experts on this subject based on the ideXlab platform.
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Optimality guarantees for distributed Statistical Estimation
arXiv: Information Theory, 2014Co-Authors: John C Duchi, Michael I Jordan, Martin J Wainwright, Yuchen ZhangAbstract:Large data sets often require performing distributed Statistical Estimation, with a full data set split across multiple machines and limited communication between machines. To study such scenarios, we define and study some refinements of the classical minimax risk that apply to distributed settings, comparing to the performance of estimators with access to the entire data. Lower bounds on these quantities provide a precise characterization of the minimum amount of communication required to achieve the centralized minimax risk. We study two classes of distributed protocols: one in which machines send messages independently over channels without feedback, and a second allowing for interactive communication, in which a central server broadcasts the messages from a given machine to all other machines. We establish lower bounds for a variety of problems, including location Estimation in several families and parameter Estimation in different types of regression models. Our results include a novel class of quantitative data-processing inequalities used to characterize the effects of limited communication.
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information theoretic lower bounds for distributed Statistical Estimation with communication constraints
Neural Information Processing Systems, 2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
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NIPS - Information-theoretic lower bounds for distributed Statistical Estimation with communication constraints
2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
Yuchen Zhang - One of the best experts on this subject based on the ideXlab platform.
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Optimality guarantees for distributed Statistical Estimation
arXiv: Information Theory, 2014Co-Authors: John C Duchi, Michael I Jordan, Martin J Wainwright, Yuchen ZhangAbstract:Large data sets often require performing distributed Statistical Estimation, with a full data set split across multiple machines and limited communication between machines. To study such scenarios, we define and study some refinements of the classical minimax risk that apply to distributed settings, comparing to the performance of estimators with access to the entire data. Lower bounds on these quantities provide a precise characterization of the minimum amount of communication required to achieve the centralized minimax risk. We study two classes of distributed protocols: one in which machines send messages independently over channels without feedback, and a second allowing for interactive communication, in which a central server broadcasts the messages from a given machine to all other machines. We establish lower bounds for a variety of problems, including location Estimation in several families and parameter Estimation in different types of regression models. Our results include a novel class of quantitative data-processing inequalities used to characterize the effects of limited communication.
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information theoretic lower bounds for distributed Statistical Estimation with communication constraints
Neural Information Processing Systems, 2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
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NIPS - Information-theoretic lower bounds for distributed Statistical Estimation with communication constraints
2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
Karan Singh - One of the best experts on this subject based on the ideXlab platform.
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robust Statistical Estimation of curvature on discretized surfaces
Symposium on Geometry Processing, 2007Co-Authors: Evangelos Kalogerakis, Patricio Simari, Derek Nowrouzezahrai, Karan SinghAbstract:A robust statistics approach to curvature Estimation on discretely sampled surfaces, namely polygon meshes and point clouds, is presented. The method exhibits accuracy, stability and consistency even for noisy, non-uniformly sampled surfaces with irregular configurations. Within an M-Estimation framework, the algorithm is able to reject noise and structured outliers by sampling normal variations in an adaptively reweighted neighborhood around each point. The algorithm can be used to reliably derive higher order differential attributes and even correct noisy surface normals while preserving the fine features of the normal and curvature field. The approach is compared with state-of-the-art curvature Estimation methods and shown to improve accuracy by up to an order of magnitude across ground truth test surfaces under varying tessellation densities and types as well as increasing degrees of noise. Finally, the benefits of a robust Statistical Estimation of curvature are illustrated by applying it to the popular applications of mesh segmentation and suggestive contour rendering.
John C Duchi - One of the best experts on this subject based on the ideXlab platform.
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Optimality guarantees for distributed Statistical Estimation
arXiv: Information Theory, 2014Co-Authors: John C Duchi, Michael I Jordan, Martin J Wainwright, Yuchen ZhangAbstract:Large data sets often require performing distributed Statistical Estimation, with a full data set split across multiple machines and limited communication between machines. To study such scenarios, we define and study some refinements of the classical minimax risk that apply to distributed settings, comparing to the performance of estimators with access to the entire data. Lower bounds on these quantities provide a precise characterization of the minimum amount of communication required to achieve the centralized minimax risk. We study two classes of distributed protocols: one in which machines send messages independently over channels without feedback, and a second allowing for interactive communication, in which a central server broadcasts the messages from a given machine to all other machines. We establish lower bounds for a variety of problems, including location Estimation in several families and parameter Estimation in different types of regression models. Our results include a novel class of quantitative data-processing inequalities used to characterize the effects of limited communication.
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information theoretic lower bounds for distributed Statistical Estimation with communication constraints
Neural Information Processing Systems, 2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
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NIPS - Information-theoretic lower bounds for distributed Statistical Estimation with communication constraints
2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
Michael I Jordan - One of the best experts on this subject based on the ideXlab platform.
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Optimality guarantees for distributed Statistical Estimation
arXiv: Information Theory, 2014Co-Authors: John C Duchi, Michael I Jordan, Martin J Wainwright, Yuchen ZhangAbstract:Large data sets often require performing distributed Statistical Estimation, with a full data set split across multiple machines and limited communication between machines. To study such scenarios, we define and study some refinements of the classical minimax risk that apply to distributed settings, comparing to the performance of estimators with access to the entire data. Lower bounds on these quantities provide a precise characterization of the minimum amount of communication required to achieve the centralized minimax risk. We study two classes of distributed protocols: one in which machines send messages independently over channels without feedback, and a second allowing for interactive communication, in which a central server broadcasts the messages from a given machine to all other machines. We establish lower bounds for a variety of problems, including location Estimation in several families and parameter Estimation in different types of regression models. Our results include a novel class of quantitative data-processing inequalities used to characterize the effects of limited communication.
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information theoretic lower bounds for distributed Statistical Estimation with communication constraints
Neural Information Processing Systems, 2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
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NIPS - Information-theoretic lower bounds for distributed Statistical Estimation with communication constraints
2013Co-Authors: Yuchen Zhang, John C Duchi, Michael I Jordan, Martin J WainwrightAbstract:We establish lower bounds on minimax risks for distributed Statistical Estimation under a communication budget. Such lower bounds reveal the minimum amount of communication required by any procedure to achieve the centralized minimax-optimal rates for Statistical Estimation. We study two classes of protocols: one in which machines send messages independently, and a second allowing for interactive communication. We establish lower bounds for several problems, including various types of location models, as well as for parameter Estimation in regression models.
